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CSE 690: GPGPU Lecture 8: Image Processing PDE Solvers

This lecture by Klaus Mueller explores the integration of GPUs in image processing and multigrid solvers. It highlights that recent GPUs conform to the IEEE floating-point standard, enhancing accuracy in computations. Key image processing techniques such as smoothing, edge detection, and averaging are discussed, demonstrating significant speedups compared to CPUs for various convolution masks. The lecture also features the multigrid solver approach presented by Cliff Woolley, showcasing efficient methods for solving boundary value problems using programmable graphics hardware.

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CSE 690: GPGPU Lecture 8: Image Processing PDE Solvers

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  1. CSE 690: GPGPULecture 8: Image Processing PDE Solvers Klaus Mueller Computer Science, Stony Brook University

  2. A Quick Note • The recent generations of GPUs do seem to conform to the IEEE floating point standard • Nvidia site explicitely confirms this for Quadro FX • R. Fernando, Eurographics 2004 (general new FX) • Older generations do not (new generations?) • see paper GPU Floating Paranoia by Hillesland/Lastra, GPGPU Workshop 2004 • IEEE sets clear conventions for rounding • round to nearest representable number (basis 2) • when tie, round to nearest even number

  3. Image Processing • Simple mask operations: • smoothing, edge detection, averaging, median • pixels under the mask are processed, combined, and written to output pixel fragment program input texture output texture

  4. Image Processing • Very streamable • no data reuse • fragments in -> fragments out • highly applicable to video processing • GPU/CPU speedup results obtained by Payne et al. (GeForce FX 5900 vs. 2.8GHz P4): • for 3x3 convolution masks and 1k x 1k images: 30 • for 5x5 masks and 1k x 1k images: 50-60 speedup • for 2k x 2k images (double HDTV) get 15 fps, while CPU gets 1.8 fps

  5. Multigrid Solver • Cliff Woolley’s presentation at the Graphics Hardware Workshop 2003 • “A Multigrid Solver for Boundary Value Problems Using Programmable Graphics Hardware” • paper by N. Goodnight, C. Woolley, G. Lewin, D. Luebke, and G. Humphries, U Virginia

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